Multimodal Deep Autoencoder for Human Pose Recovery

Video-based human pose recovery is usually conducted by retrieving relevant poses using image features. In the retrieving process, the mapping between 2D images and 3D poses is assumed to be linear in most of the traditional methods. However, their relationships are inherently non-linear, which limi...

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Vydáno v:	IEEE transactions on image processing Ročník 24; číslo 12; s. 5659 - 5670
Hlavní autoři:	Hong, Chaoqun, Yu, Jun, Wan, Jian, Tao, Dacheng, Wang, Meng
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	United States IEEE 01.12.2015 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:	Artificial Intelligence Back propagation deep learning Electronic mail Feature extraction Female Graphs Hidden Markov models Human Human pose recovery Humans hypergraph Image Processing, Computer-Assisted - methods Machine learning Male Mapping multi-modal learning Neural networks Nonlinearity Posture - physiology Recovery Three dimensional Three-dimensional displays Two dimensional Visualization deep learning hypergraph Human pose recovery multi-modal learning back propagation
ISSN:	1057-7149, 1941-0042, 1941-0042
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Popis
Shrnutí:	Video-based human pose recovery is usually conducted by retrieving relevant poses using image features. In the retrieving process, the mapping between 2D images and 3D poses is assumed to be linear in most of the traditional methods. However, their relationships are inherently non-linear, which limits recovery performance of these methods. In this paper, we propose a novel pose recovery method using non-linear mapping with multi-layered deep neural network. It is based on feature extraction with multimodal fusion and back-propagation deep learning. In multimodal fusion, we construct hypergraph Laplacian with low-rank representation. In this way, we obtain a unified feature description by standard eigen-decomposition of the hypergraph Laplacian matrix. In back-propagation deep learning, we learn a non-linear mapping from 2D images to 3D poses with parameter fine-tuning. The experimental results on three data sets show that the recovery error has been reduced by 20%-25%, which demonstrates the effectiveness of the proposed method.
Bibliografie:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23
ISSN:	1057-7149 1941-0042 1941-0042
DOI:	10.1109/TIP.2015.2487860